Improved embedding geometry.
Several works have studied means of improving the geometry of learned embeddings for clasification tasks.
The center loss [1]} pulls sample embeddings towards their class
centroid \(\mu _y\) :
\( \Omega _{\rm center}( x, y ) \stackrel{.}{=}\Vert  \Phi ( x ) - \mu _y \Vert _2^2. \) 
